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首页> 外文期刊>International Journal of Advanced Robotic Systems >Scheduling unmanned aerial vehicle and automated guided vehicle operations in an indoor manufacturing environment using differential evolution-fused particle swarm optimization
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Scheduling unmanned aerial vehicle and automated guided vehicle operations in an indoor manufacturing environment using differential evolution-fused particle swarm optimization

机译：使用差分进化融合粒子群算法在室内制造环境中调度无人机和自动制导车辆的运行

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摘要

Intelligent manufacturing technologies have been pursued by the industries to establish an autonomous indoor manufacturing environment. It means that tasks, which are comprised in the desired manufacturing activities, shall be performed with exceptional human interventions. This entails the employment of automated resources (i.e. machines) and agents (i.e. robots) on the shop floor. Such an implementation requires a planning system which controls the actions of the agents and their interactions with the resources to accomplish a given set of tasks. A scheduling system which plans the task executions by scheduling the available unmanned aerial vehicles and automated guided vehicles is investigated in this study. The primary objective of the study is to optimize the schedule in a cost-efficient manner. This includes the minimization of makespan and total battery consumption; the priority is given to the schedule with the better makespan. A metaheuristic-based methodology called differential evolution-fused particle swarm optimization is proposed, whose performance is benchmarked with several data sets. Each data set possesses different weights upon characteristics such as geographical scale, number of predecessors, and number of tasks. Differential evolution-fused particle swarm optimization is compared against differential evolution and particle swarm optimization throughout the conducted numerical simulations. It is shown that differential evolution-fused particle swarm optimization is effective to tackle the addressed problem, in terms of objective values and computation time.
机译：工业界一直在追求智能制造技术，以建立自主的室内制造环境。这意味着，必须在特殊的人工干预下执行所需制造活动中包含的任务。这需要在车间使用自动化资源（即机器）和代理（即机器人）。这样的实现需要计划系统，该系统控制代理的动作以及它们与资源的交互以完成给定的一组任务。在这项研究中，研究了一种调度系统，该系统通过调度可用的无人机和自动引导车辆来计划任务执行。该研究的主要目的是以经济高效的方式优化计划。这包括最小化制造时间和总电池消耗;优先考虑的是具有更好的制造时间的进度表。提出了一种基于元启发式的方法，称为差分进化融合粒子群优化，其性能以多个数据集为基准。每个数据集在特征上都具有不同的权重，例如地理范围，前任者的数量和任务的数量。在整个进行的数值模拟中，将差分进化融合的粒子群优化与差分进化和粒子群优化进行了比较。结果表明，基于目标值和计算时间，差分进化融合粒子群算法可以有效地解决上述问题。

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